Sold by: John Snow LabsÂ
Deployed on AWS
Free Trial
Use for chat, RAG, medical summarization, open-book question answering with context of up to 32K tokens.
Overview
Trained on diverse medical texts, this model excels in summarizing, answering complex clinical questions, and transforming clinical notes, patient encounters, and medical reports into concise summaries.
Its question-answering capability ensures context-specific responses, enhancing decision-making.
For physicians, this tool offers a quick grasp of a patient history, aiding timely decisions.
Optimized for Retrieval-Augmented Generation (RAG), the model integrates with healthcare databases, EHRs, and PubMed to boost response quality.
For enhanced patient care, we offer clinical de-identification for secure data processing, medical speech-to-text for accurate transcriptions, and a medical chatbot to facilitate patient interaction.
IMPORTANT USAGE INFORMATION:
After subscribing to this product and creating a SageMaker endpoint, billing occurs on an HOURLY BASIS for as long as the endpoint is running.
-Charges apply even if the endpoint is idle and not actively processing requests.
-To stop charges, you MUST DELETE the endpoint in your SageMaker console.
-Simply stopping requests will NOT stop billing.
This ensures you are only billed for the time you actively use the service.
Highlights
- **Benchmarking Results:** * Achieves 86.31% average on OpenMed benchmarks, surpassing GPT-4 (82.85%) and Med-PaLM-2 (84.08%) * Medical genetics: 95%; performance in professional medicine: 94.85% * Clinical knowledge comprehension 89.81% and college biology mastery 93.75% * Achieves 58.9% average on standard LLM benchmarks * Balance of specialized medical knowledge and language understanding - 70.93% on GPT4All benchmark * Achieves 75.54% performance in medical MCQAs and 79.4% on PubMedQA
- **Real-Time Inference** * Instance Type: ml.p4d.24xlarge * Maximum context length for this instance type: 32k * Tokens per Second during real-time inference: * **QA**: up to 550 tokens per second * **Summarization**: up to 130 tokens per second * Instance Type: ml.p5.48xlarge * Maximum supported context length for this instance type: 32k * Tokens per Second during real-time inference: * **QA**: up to 1028 tokens per second * **Summarization**: up to 230 tokens per second
- **Video materials:** * [Medical Language Models as AWS SageMaker private API endpoints](https://www.youtube.com/watch?v=i04iYe4U9C0&ab_channel=JohnSnowLabs) * [Introduction to Medical Language Models and Benchmarks with Healthcare NLP](https://www.youtube.com/watch?v=Nak5Mn96bNI&ab_channel=JohnSnowLabs) * [Medical Language Models Deployment Options Use Case: Medical Chatbot](https://www.youtube.com/watch?v=RyCWoxpdDJY&ab_channel=JohnSnowLabs)
Details
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Latest version
Deployed on AWS
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Pricing
Free trial
Try this product free for 15 days according to the free trial terms set by the vendor.
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.g5.48xlarge Inference (Batch) Recommended | Model inference on the ml.g5.48xlarge instance type, batch mode | $19.96 |
ml.p4d.24xlarge Inference (Real-Time) Recommended | Model inference on the ml.p4d.24xlarge instance type, real-time mode | $19.96 |
ml.g4dn.12xlarge Inference (Batch) | Model inference on the ml.g4dn.12xlarge instance type, batch mode | $19.96 |
ml.g5.2xlarge Inference (Batch) | Model inference on the ml.g5.2xlarge instance type, batch mode | $19.96 |
ml.g4dn.12xlarge Inference (Real-Time) | Model inference on the ml.g4dn.12xlarge instance type, real-time mode | $19.96 |
ml.g6.48xlarge Inference (Real-Time) | Model inference on the ml.g6.48xlarge instance type, real-time mode | $19.96 |
ml.g5.2xlarge Inference (Real-Time) | Model inference on the ml.g5.2xlarge instance type, real-time mode | $19.96 |
ml.p5.48xlarge Inference (Real-Time) | Model inference on the ml.p5.48xlarge instance type, real-time mode | $19.96 |
Vendor refund policy
No refunds are possible.
Legal
Vendor terms and conditions
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Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
Model optimization.
Additional details
Inputs
- Summary
Input Format
1. Chat Completion
Example Payload
{
"model": "/opt/ml/model",
"messages": [
{"role": "system", "content": "You are a helpful medical assistant."},
{"role": "user", "content": "What should I do if I have a fever and body aches?"}
],
"max_tokens": 1024,
"temperature": 0.7
}2. Text Completion
Single Prompt Example
{
"model": "/opt/ml/model",
"prompt": "How can I maintain good kidney health?",
"max_tokens": 512,
"temperature": 0.6
}Multiple Prompts Example
{
"model": "/opt/ml/model",
"prompt": [
"How can I maintain good kidney health?",
"What are the best practices for kidney care?"
],
"max_tokens": 512,
"temperature": 0.6
}Important Notes:
- Streaming Responses: Add "stream": true to your request payload to enable streaming
- Model Path Requirement: Always set "model": "/opt/ml/model" (SageMaker's fixed model location)
For addistional details check the documentation hereÂ
- Input MIME type
- application/json
https://github.com/JohnSnowLabs/spark-nlp-workshop/tree/master/products/sagemaker/models/JSL-Medical-LLM-Medium/inputs/real-time
https://github.com/JohnSnowLabs/spark-nlp-workshop/tree/master/products/sagemaker/models/JSL-Medical-LLM-Medium/inputs/batch
Resources
Support
Vendor support
For any assistance, please reach out to support@johnsnowlabs.com .
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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